One-dimensional Multiferroic Semiconductor WOI3: Unconventional Anisotropic d^1 Rule and Bulk Photovoltaic Effect

The pursuit of multiferroic magnetoelectrics, combining simultaneous ferroelectric and magnetic orders, remains a central focus in condensed matter physics. Here we report the centrosymmetric, one-dimensional (1D) antiferromagnetic WOI$_3$ undergoes a strain-induced ferroelectric distortion. The paraelectric-ferroelectric transition is originated from the unconventional anisotropic $d^1$ mechanism, where an unpaired d electron of each W$^{5+}$ ion contributes to magnetic orders. Employing a Heisenberg model with Dzyaloshinskii-Moriya interaction, we predict an antiferromagnetic spin configuration as the paraelectric ground state, transitioning to a ferroelectric phase with noncollinear spin arrangement under uniaxial strain. The ferroelectric polarization and noncollinear spin arrangement can be manipulated by varying the applied strain. While the energy barriers for switching ferroelectric polarizations with magnetic orders are on the order of a few dozen of meV, the shift current bulk photovoltaic effect (BPVE) exhibits remarkable differences, providing a precise and valuable tool for experimentally probing the interplay of ferroelectric and magnetic orders in 1D WOI$_3$.Comment: 19 pages, 5 figure

A Dense Convolutional Bi-Mamba Framework for EEG-Based Emotion Recognition

In recent times, emotion recognition based on electroencephalograms (EEGs) has found extensive applications. Although numerous approaches leveraging CNN and Transformer have been put forward for automatic emotion recognition and have achieved commendable performance, several challenges remain: (1) Transformer-based models are proficient at capturing long-term dependencies within EEG signals. However, their quadratic computational complexity poses a significant hurdle. (2) Models that combine Transformers with convolutional neural networks (CNNs) often fail to effectively capture the coarse-to-fine temporal dynamics of EEG signals. State Space Models (SSMs), exemplified by Mamba, have emerged as a promising solution. They not only showcase outstanding capabilities in modeling long-range interactions but also maintain a linear computational complexity, which is highly advantageous. To address these challenges head-on, we introduce Emotion-Mamba, an innovative framework designed specifically for EEG-based emotion recognition. The proposed framework initiates the process by employing the CNN Encoder to extract information from both the temporal and spatial dimensions of EEG signals. Subsequently, the extracted feature information is relayed to the Hierarchical Coarse-to-Fine Bi-Mamba (HBM) block, which is adept at efficiently processing these features. Furthermore, a Dense Temporal Fusion (DTF) module has been incorporated. This module capitalizes on the multi-level, purified temporal information sourced from CNN Encoder and HBM blocks, with the aim of bolstering decoding accuracy. We conduct comprehensive evaluations of Emotion-Mamba using the SEED and SEED-V datasets. The experimental findings unequivocally demonstrate that our proposed approach surpasses the existing state-of-the-art methods

Helix: Distributed Serving of Large Language Models via Max-Flow on Heterogeneous GPUs

This paper introduces Helix, a distributed system for high-throughput, low-latency large language model (LLM) serving on heterogeneous GPU clusters. A key idea behind Helix is to formulate inference computation of LLMs over heterogeneous GPUs and network connections as a max-flow problem for a directed, weighted graph, whose nodes represent GPU instances and edges capture both GPU and network heterogeneity through their capacities. Helix then uses a mixed integer linear programming (MILP) algorithm to discover highly optimized strategies to serve LLMs. This approach allows Helix to jointly optimize model placement and request scheduling, two highly entangled tasks in heterogeneous LLM serving. Our evaluation on several heterogeneous cluster settings ranging from 24 to 42 GPU nodes shows that Helix improves serving throughput by up to 2.7$\times$ and reduces prompting and decoding latency by up to 2.8$\times$ and 1.3$\times$, respectively, compared to best existing approaches

Enhanced visual SLAM for collision-free driving with lightweight autonomous cars

The paper presents a vision-based obstacle avoidance strategy for lightweight self-driving cars that can be run on a CPU-only device using a single RGB-D camera. The method consists of two steps: visual perception and path planning. The visual perception part uses ORBSLAM3 enhanced with optical flow to estimate the car’s poses and extract rich texture information from the scene. In the path planning phase, the proposed method employs a method combining a control Lyapunov function and control barrier function in the form of a quadratic program (CLF-CBF-QP) together with an obstacle shape reconstruction process (SRP) to plan safe and stable trajectories. To validate the performance and robustness of the proposed method, simulation experiments were conducted with a car in various complex indoor environments using the Gazebo simulation environment. The proposed method can effectively avoid obstacles in the scenes. The proposed algorithm outperforms benchmark algorithms in achieving more stable and shorter trajectories across multiple simulated scenes

A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts

Safety and efficiency are crucial for autonomous driving in roundabouts, especially in the context of mixed traffic where autonomous vehicles (AVs) and human-driven vehicles coexist. This paper introduces a learning-based algorithm tailored to foster safe and efficient driving behaviors across varying levels of traffic flows in roundabouts. The proposed algorithm employs a deep Q-learning network to effectively learn safe and efficient driving strategies in complex multi-vehicle roundabouts. Additionally, a KAN (Kolmogorov-Arnold network) enhances the AVs' ability to learn their surroundings robustly and precisely. An action inspector is integrated to replace dangerous actions to avoid collisions when the AV interacts with the environment, and a route planner is proposed to enhance the driving efficiency and safety of the AVs. Moreover, a model predictive control is adopted to ensure stability and precision of the driving actions. The results show that our proposed system consistently achieves safe and efficient driving whilst maintaining a stable training process, as evidenced by the smooth convergence of the reward function and the low variance in the training curves across various traffic flows. Compared to state-of-the-art benchmarks, the proposed algorithm achieves a lower number of collisions and reduced travel time to destination.Comment: 15 pages, 12 figures, submitted to an IEEE journa

A conflicts-free, speed-lossless KAN-based reinforcement learning decision system for interactive driving in roundabouts

Safety and efficiency are crucial for autonomous driving in roundabouts, especially mixed traffic with both autonomous vehicles (AVs) and human-driven vehicles. This paper presents a learning-based algorithm that promotes safe and efficient driving across varying roundabout traffic conditions. A deep Q-learning network is used to learn optimal strategies in complex multi-vehicle roundabout scenarios, while a Kolmogorov-Arnold Network (KAN) improves the AVs’ environmental understanding. To further enhance safety, an action inspector filters unsafe actions, and a route planner optimizes driving efficiency. Moreover, model predictive control ensures stability and precision in execution. Experimental results demonstrate that the proposed system consistently outperforms state-of-the-art methods, achieving fewer collisions, reduced travel time, and stable training with smooth reward convergence

BRCA1-Mediated Dual Regulation of Ferroptosis Exposes a Vulnerability to GPX4 and PARP Co-Inhibition in BRCA1-Deficient Cancers

Resistance to poly (ADP-ribose) polymerase inhibitors (PARPi) limits the therapeutic efficacy of PARP inhibition in treating breast cancer susceptibility gene 1 (BRCA1)-deficient cancers. Here we reveal that BRCA1 has a dual role in regulating ferroptosis. BRCA1 promotes the transcription of voltage-dependent anion channel 3 (VDAC3) and glutathione peroxidase 4 (GPX4); consequently, BRCA1 deficiency promotes cellular resistance to erastin-induced ferroptosis but sensitizes cancer cells to ferroptosis induced by GPX4 inhibitors (GPX4i). In addition, nuclear receptor coactivator 4 (NCOA4)-mediated ferritinophagy and defective GPX4 induction unleash potent ferroptosis in BRCA1-deficient cancer cells upon PARPi and GPX4i co-treatment. Finally, we show that xenograft tumors derived from BRCA1-mutant breast cancer patients with PARPi resistance exhibit decreased GPX4 expression and high sensitivity to PARP and GPX4 co-inhibition. Our results show that BRCA1 deficiency induces a ferroptosis vulnerability to PARP and GPX4 co-inhibition and inform a therapeutic strategy for overcoming PARPi resistance in BRCA1-deficient cancers